We need to find a range of values to appropriately label events of concern.
We use,
as a surrogate to label a concern event.
Conclusions:
where \(\bf{Pc\_warn}\) is the Collision Probability that triggers a warning.
We need to evaluate warning thresholds by examining the trade space between risk aversion and tolerance.
We have the following working definitions:
\(\hspace{2.5cm}\bf{\text{False Negative (FN)} := \text{# of Concern Events in a year that did not trigger a warning}}\)
\(\hspace{2.5cm}\bf{\text{False Positive (FP)} := \text{# of False Alarms in a year}}\).
We can now explore how the warning threshold affects the \(\text{FP}\) and \(\text{FN}\).
Recall a higher concentration of events that remain stable at three days to TCA. This implies that as TCA approaches, the Collision Probability changes less, which means our ability to correctly warn should improve. We explore short notice warning performance at 4, 3, and 2 days to TCA.